Work

Machine learning-assisted three-dimensional mapping of the facial soft tissues in Saudi Arabian adults with skeletal class I relationship

Year: 2026

Type: article

Abstract: OBJECTIVES: This study aimed to evaluate facial soft-tissue features in adults with skeletal Class I relationships using three-dimensional (3D) imaging and machine learning to identify sex-specific anthropometric patterns and provide reference data for orthodontic assessments. MATERIALS AND METHODS: A total of 94 Saudi Arabian adults (50 men, mean age 25 ± 2.1 years; 44 women, mean age 23 ± 1.9 years) with Class I skeletal and dental relationships and body mass index (BMI) <25 kg/m² were included in this cross-sectional study. Standardized 3D facial photographs were obtained using a radiation-free system (ProMax 3D ProFace, Planmeca Oy, Helsinki, Finland). Sex differences were evaluated using conventional statistical tests. An L1-regularized logistic regression model was applied for feature selection and sex classification. Model performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). RESULTS: < 0.001). Women showed higher vermilion height-to-mouth width and upper face height-to-mandibular width ratios. The machine learning model classified sex with high accuracy (91.3% ± 6.2%), precision (96.0% ± 8.0%), recall (85.5% ± 11.9%), F1-score (89.7% ± 7.0%), and AUC (0.91 ± 0.07). CONCLUSIONS: This study demonstrates that combining 3D facial imaging with machine learning effectively captures sexual dimorphism in a Saudi Arabian adult population. The findings provide clinically relevant reference data for personalized orthodontic assessment and treatment planning and support forensic applications.

Source: Journal of Orthodontic Science

DOI: 10.4103/jos.jos_111_25